Predictive data mining modeling techniques generally include building a classification or regression model that can accurately predict values by observing the values of input attributes. Finding an optimal algorithm and its control parameters for building a predictive data mining model is difficult for various reasons. For example, there are many possible classification algorithms and associated control parameters. Also, it is very time consuming to build a model for datasets containing a large number of records and attributes. These two reasons, among various others, make it impractical to find an optimal model by enumerating through a large number of algorithms and their possible control parameters. Thus, there remains room for improvement in current predictive data mining modeling techniques.